Prepare data
Prevalence
df_ger_prev <- read_csv('Germany_timeseries_prep.csv')
Parsed with column specification:
cols(
date = [31mcol_character()[39m,
anzahlfall = [32mcol_double()[39m,
kreis = [32mcol_double()[39m,
ewz = [32mcol_double()[39m,
shape__area = [32mcol_double()[39m,
cumcase = [32mcol_double()[39m,
kreis_name = [31mcol_character()[39m,
extra = [32mcol_double()[39m,
agree = [32mcol_double()[39m,
sci = [32mcol_double()[39m,
neuro = [32mcol_double()[39m,
open = [32mcol_double()[39m,
rate_day = [32mcol_double()[39m,
popdens = [32mcol_double()[39m,
runday = [32mcol_double()[39m
)
df_ger_prev <- df_ger_prev %>% mutate(date = as.Date(date, "%d%b%Y"),
kreis = as.character(kreis)) %>%
dplyr::select(kreis, date, rate_day)
df_ger_prev
Scoial distancing
df_ger_socdist$date %>% summary()
Min. 1st Qu. Median Mean 3rd Qu. Max.
"2020-02-25" "2020-03-11" "2020-03-27" "2020-03-27" "2020-04-12" "2020-04-27"
Personality
df_ger_pers <- read_csv('Germany_timeseries_prep.csv')
Parsed with column specification:
cols(
date = [31mcol_character()[39m,
anzahlfall = [32mcol_double()[39m,
kreis = [32mcol_double()[39m,
ewz = [32mcol_double()[39m,
shape__area = [32mcol_double()[39m,
cumcase = [32mcol_double()[39m,
kreis_name = [31mcol_character()[39m,
extra = [32mcol_double()[39m,
agree = [32mcol_double()[39m,
sci = [32mcol_double()[39m,
neuro = [32mcol_double()[39m,
open = [32mcol_double()[39m,
rate_day = [32mcol_double()[39m,
popdens = [32mcol_double()[39m,
runday = [32mcol_double()[39m
)
df_ger_pers <- df_ger_pers %>%
select(kreis, open, sci, extra, agree, neuro) %>%
dplyr::rename(pers_o = open,
pers_c = sci,
pers_e = extra,
pers_a = agree,
pers_n = neuro) %>%
distinct() %>%
mutate(kreis = as.character(kreis))
df_ger_pers
NA
Controls
df_ger_ctrl <- read.csv2('Germany_controls.csv', sep = ';', dec=',')
df_ger_ctrl <- df_ger_ctrl %>% select(-kreis_nme) %>%
mutate(kreis = as.character(kreis),
popdens = popdens %>%
as.character() %>%
str_replace('\\.', '')%>%
as.numeric())
df_ger_ctrl
NA
Merge prevalence data
# create sequence of dates
date_sequence <- seq.Date(min(df_ger_prev$date),
max(df_ger_prev$date), 1)
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence))
names(df_dates) <- c('date', 'time')
# merge prevalence data
df_ger_prev <- df_ger_prev %>%
inner_join(df_ger_pers, by = 'kreis') %>%
inner_join(df_ger_ctrl, by = 'kreis') %>%
merge(df_dates, by='date') %>%
arrange(kreis)
df_ger_prev
Merge socdist data
# create sequence of dates
date_sequence <- seq.Date(min(df_ger_socdist$date),
max(df_ger_socdist$date), 1)
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence))
names(df_dates) <- c('date', 'time')
# merge socdist data
df_ger_socdist <- df_ger_socdist %>%
inner_join(df_ger_pers, by = 'kreis') %>%
inner_join(df_ger_ctrl, by = 'kreis') %>%
merge(df_dates, by='date') %>%
arrange(kreis)
df_ger_socdist
NA
Control for weekend effect
easter <- seq.Date(as.Date('2020-04-10'), as.Date('2020-04-13'), 1)
df_ger_loess <- df_ger_socdist %>%
mutate(weekday = format(date, '%u')) %>%
filter(!(weekday %in% c('6','7') | date %in% easter)) %>%
split(.$kreis) %>%
map(~ loess(socdist_single_tile ~ time, data = .)) %>%
map(predict, 1:max(df_ger_socdist$time)) %>%
bind_rows() %>%
gather(key = 'kreis', value = 'loess') %>%
group_by(kreis) %>%
mutate(time = row_number())
df_ger_socdist <- df_ger_socdist %>% merge(df_ger_loess, by=c('kreis', 'time')) %>%
mutate(weekday = format(date, '%u')) %>%
mutate(socdist_single_tile_clean = ifelse(weekday %in% c('6','7') | date %in% easter,
loess, socdist_single_tile)) %>%
arrange(kreis, time) %>%
select(-weekday)
df_ger_socdist %>% drop_na()
NA
Explore data
Plot prevalence over time
df_ger_prev %>% ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=kreis, size=popdens)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall prevalence over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_ger_prev %>% mutate(prev_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(prev_tail != 'center') %>%
ggplot(aes(x=time, y=rate_day)) +
geom_point(aes(col=kreis, size=popdens)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~prev_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





Plot social distancing (single tile) over time
df_ger_socdist %>% ggplot(aes(x=time, y=socdist_single_tile_clean)) +
geom_point(aes(col=kreis, size=popdens)) +
geom_smooth(method="loess", se=T) +
theme(legend.position="none") +
ggtitle("Overall social distancing (single tile) over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')
for (i in pers){
gg <- df_ger_socdist %>% mutate(prev_tail = cut(.[[i]],
breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
labels = c('lower tail', 'center', 'upper tail'))) %>%
filter(prev_tail != 'center') %>%
ggplot(aes(x=time, y=socdist_single_tile_clean)) +
geom_point(aes(col=kreis, size=popdens)) +
geom_smooth(method="loess", se=T) +
facet_wrap(~prev_tail) +
theme(legend.position="none") +
ggtitle(i)
print(gg)
}





df_ger_socdist <- df_ger_socdist %>% mutate(socdist_single_tile = socdist_single_tile_clean) %>%
select(-loess, -socdist_single_tile_clean)
Correlations
df_ger_prev %>% group_by(kreis) %>%
summarize_if(is.numeric, mean) %>%
select(-kreis, -time) %>%
cor(use='pairwise.complete') %>%
round(3) %>% as.data.frame()
df_ger_socdist %>% group_by(kreis) %>%
summarize_if(is.numeric, mean) %>%
select(-kreis, -time) %>%
cor(use='pairwise.complete') %>%
round(3) %>% as.data.frame()
NA
NA
Rescale data
lvl2_scaled <- df_ger_prev %>%
dplyr::select(-time, -rate_day, -date) %>%
distinct() %>%
mutate_at(vars(-kreis), scale)
lvl1_scaled <- df_ger_prev %>% select(kreis, time, rate_day)
df_ger_prev_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'kreis')
df_ger_prev_scaled
NA
lvl2_scaled <- df_ger_socdist %>%
dplyr::select(-time, -socdist_single_tile, -date) %>%
distinct() %>%
mutate_at(vars(-kreis), scale)
lvl1_scaled <- df_ger_socdist %>% select(kreis, time, socdist_single_tile)
df_ger_socdist_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'kreis')
df_ger_socdist_scaled
NA
Predict Prevalence
Explore distributions
df_ger_onset_prev %>% ggplot(aes(onset_prev)) + geom_histogram()

df_ger_slope_prev %>% ggplot(aes(slope_prev)) + geom_histogram()

Predict COVID onset with time-to-event regression
# predict onset from personality
cox_onset_prev <- coxph(Surv(onset_prev, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_ger_onset_prev)
cox_onset_prev %>% summary()
Call:
coxph(formula = Surv(onset_prev, event) ~ pers_o + pers_c + pers_e +
pers_a + pers_n, data = df_ger_onset_prev)
n= 400, number of events= 400
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.131653 1.140712 0.051953 2.534 0.0113 *
pers_c -0.104500 0.900775 0.053314 -1.960 0.0500 *
pers_e 0.056186 1.057794 0.052903 1.062 0.2882
pers_a -0.008624 0.991413 0.049615 -0.174 0.8620
pers_n -0.276799 0.758207 0.057728 -4.795 1.63e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.1407 0.8766 1.0303 1.263
pers_c 0.9008 1.1102 0.8114 1.000
pers_e 1.0578 0.9454 0.9536 1.173
pers_a 0.9914 1.0087 0.8995 1.093
pers_n 0.7582 1.3189 0.6771 0.849
Concordance= 0.592 (se = 0.018 )
Likelihood ratio test= 39.92 on 5 df, p=2e-07
Wald test = 37.99 on 5 df, p=4e-07
Score (logrank) test = 37.99 on 5 df, p=4e-07
# predict onset from personality with controls
cox_onset_prev_ctrl <- coxph(Surv(onset_prev, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_onset_prev)
cox_onset_prev_ctrl %>% summary()
Call:
coxph(formula = Surv(onset_prev, event) ~ pers_o + pers_c + pers_e +
pers_a + pers_n + women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age + popdens,
data = df_ger_onset_prev)
n= 392, number of events= 392
(8 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.050510 1.051807 0.059853 0.844 0.398726
pers_c -0.087556 0.916168 0.061192 -1.431 0.152473
pers_e 0.038883 1.039649 0.057895 0.672 0.501825
pers_a 0.008022 1.008054 0.057017 0.141 0.888110
pers_n -0.222357 0.800630 0.063469 -3.503 0.000459 ***
women 0.066190 1.068429 0.067498 0.981 0.326785
academics 0.267424 1.306595 0.094669 2.825 0.004730 **
afd -0.072815 0.929773 0.075881 -0.960 0.337260
hospital_beds -0.236175 0.789642 0.069836 -3.382 0.000720 ***
tourism_beds 0.076981 1.080022 0.054241 1.419 0.155829
gdp -0.175570 0.838979 0.115831 -1.516 0.129586
manufact 0.147898 1.159395 0.094458 1.566 0.117404
airport -0.171432 0.842457 0.061687 -2.779 0.005451 **
age -0.176055 0.838572 0.085439 -2.061 0.039343 *
popdens 0.062986 1.065012 0.082801 0.761 0.446841
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.0518 0.9507 0.9354 1.1827
pers_c 0.9162 1.0915 0.8126 1.0329
pers_e 1.0396 0.9619 0.9281 1.1646
pers_a 1.0081 0.9920 0.9015 1.1272
pers_n 0.8006 1.2490 0.7070 0.9067
women 1.0684 0.9360 0.9360 1.2196
academics 1.3066 0.7653 1.0853 1.5730
afd 0.9298 1.0755 0.8013 1.0789
hospital_beds 0.7896 1.2664 0.6886 0.9055
tourism_beds 1.0800 0.9259 0.9711 1.2012
gdp 0.8390 1.1919 0.6686 1.0528
manufact 1.1594 0.8625 0.9634 1.3952
airport 0.8425 1.1870 0.7465 0.9507
age 0.8386 1.1925 0.7093 0.9914
popdens 1.0650 0.9390 0.9055 1.2527
Concordance= 0.658 (se = 0.016 )
Likelihood ratio test= 101.3 on 15 df, p=7e-15
Wald test = 98.22 on 15 df, p=3e-14
Score (logrank) test = 102.1 on 15 df, p=5e-15
Predict prevalence slopes with linear models
# predict slopes from personality
lm_slope_prev <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_ger_slope_prev)
lm_slope_prev %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_ger_slope_prev)
Residuals:
Min 1Q Median 3Q Max
-1.1994 -0.5725 -0.1888 0.2940 8.6104
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.00174 0.05054 0.034 0.973
pers_o -0.03181 0.05420 -0.587 0.558
pers_c -0.05989 0.05799 -1.033 0.302
pers_e 0.07814 0.05745 1.360 0.175
pers_a 0.09131 0.05839 1.564 0.119
pers_n 0.06209 0.05971 1.040 0.299
Residual standard error: 1 on 386 degrees of freedom
Multiple R-squared: 0.01215, Adjusted R-squared: -0.0006472
F-statistic: 0.9494 on 5 and 386 DF, p-value: 0.4489
lm_slope_prev %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.081594195 0.08507360
pers_o -0.121173276 0.05756145
pers_c -0.155494307 0.03572519
pers_e -0.016586494 0.17286042
pers_a -0.004968057 0.18758968
pers_n -0.036363942 0.16053810
# predict slopes from personality with controls
lm_slope_prev_ctrl <- lm(slope_prev ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_slope_prev)
lm_slope_prev_ctrl %>% summary()
Call:
lm(formula = slope_prev ~ pers_o + pers_c + pers_e + pers_a +
pers_n + women + academics + afd + hospital_beds + tourism_beds +
gdp + manufact + airport + age + popdens, data = df_ger_slope_prev)
Residuals:
Min 1Q Median 3Q Max
-1.4465 -0.5788 -0.0848 0.3212 8.0256
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0005982 0.0488695 -0.012 0.9902
pers_o -0.0160080 0.0603371 -0.265 0.7909
pers_c 0.0018010 0.0584550 0.031 0.9754
pers_e 0.0769996 0.0572311 1.345 0.1793
pers_a 0.0832263 0.0586703 1.419 0.1569
pers_n 0.0621839 0.0593346 1.048 0.2953
women 0.1316729 0.0715869 1.839 0.0667 .
academics -0.0618388 0.0955468 -0.647 0.5179
afd -0.0225847 0.0712477 -0.317 0.7514
hospital_beds 0.0939475 0.0657371 1.429 0.1538
tourism_beds -0.0400654 0.0550991 -0.727 0.4676
gdp -0.0130746 0.1126142 -0.116 0.9076
manufact 0.1729185 0.0894459 1.933 0.0540 .
airport 0.1503036 0.0612082 2.456 0.0145 *
age -0.1912130 0.0908032 -2.106 0.0359 *
popdens -0.1393613 0.0790525 -1.763 0.0787 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9665 on 376 degrees of freedom
Multiple R-squared: 0.1016, Adjusted R-squared: 0.06581
F-statistic: 2.836 on 15 and 376 DF, p-value: 0.0003208
lm_slope_prev_ctrl %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.08118002 0.079983552
pers_o -0.11549884 0.083482831
pers_c -0.09458638 0.098188366
pers_e -0.01736960 0.171368869
pers_a -0.01351600 0.179968683
pers_n -0.03565397 0.160021700
women 0.01363199 0.249713732
academics -0.21938756 0.095709935
afd -0.14006616 0.094896812
hospital_beds -0.01444736 0.202342442
tourism_beds -0.13091924 0.050788398
gdp -0.19876601 0.172616820
manufact 0.02542968 0.320407240
airport 0.04937648 0.251230766
age -0.34093993 -0.041486083
popdens -0.26971227 -0.009010392
CRF predicting slopes
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_slope_prev <- cforest(slope_prev ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_slope_prev,
controls = ctrls)
crf_slope_prev_varimp <- varimp(crf_slope_prev, nperm = 1)
crf_slope_prev_varimp_cond <- varimp(crf_slope_prev, conditional = T, nperm = 1)
crf_slope_prev_varimp
pers_o pers_c pers_e pers_a pers_n women academics
0.001488878 -0.004182147 0.001303730 0.012206527 0.001014518 0.009796793 0.003829958
afd hospital_beds tourism_beds gdp manufact airport age
0.015500844 0.018005690 -0.001781128 0.021456347 0.035828919 0.017943409 0.023111995
popdens
0.011791481
crf_slope_prev_varimp %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_slope_prev_varimp_cond
pers_o pers_c pers_e pers_a pers_n women academics
-0.0002143011 -0.0014382430 -0.0047726834 0.0097148040 0.0009568305 0.0163477254 0.0051396731
afd hospital_beds tourism_beds gdp manufact airport age
0.0163462437 0.0164595151 -0.0030080531 0.0227972666 0.0308178428 0.0157462891 0.0238096354
popdens
0.0150246388
crf_slope_prev_varimp_cond %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Predict Social Distancing
Change point analysis
# keep only counties with full data
kreis_complete <- df_ger_socdist_scaled %>%
group_by(kreis) %>%
summarize(n = n()) %>%
filter(n==max(.$n)) %>%
.$kreis
# run changepoint analysis
df_ger_socdist_cpt_results <- df_ger_socdist_scaled %>%
select(kreis, socdist_single_tile) %>%
filter(kreis %in% kreis_complete) %>%
split(.$kreis) %>%
map(~ cpt.meanvar(as.vector(.$socdist_single_tile),
#penalty = 'Asymptotic',
class=TRUE,
param.estimates=TRUE,
Q=1,
test.stat = 'Normal'))
# calculate change point
df_ger_socdist_cpt_day <- df_ger_socdist_cpt_results %>%
map(cpts) %>%
unlist() %>%
as.data.frame() %>%
rename(cpt_day_socdist = '.') %>%
rownames_to_column('kreis')
# calculate mean differences
df_ger_socdist_cpt_mean_diff <- df_ger_socdist_cpt_results %>%
map(param.est) %>%
map(~ .$mean) %>%
map(~ .[2]-.[1]) %>%
unlist() %>%
as.data.frame() %>%
rename(mean_diff_socdist = '.') %>%
rownames_to_column('kreis')
# calculate varaince differences
df_ger_socdist_cpt_var_diff <- df_ger_socdist_cpt_results %>%
map(param.est) %>%
map(~ .$variance) %>%
map(~ .[2]-.[1]) %>%
unlist() %>%
as.data.frame() %>%
rename(var_diff_socdist = '.') %>%
rownames_to_column('kreis')
# merge with county data
df_ger_cpt_socdist <- df_ger_socdist_scaled %>%
select(-time, -socdist_single_tile) %>%
distinct() %>%
left_join(df_ger_socdist_cpt_day, by='kreis') %>%
left_join(df_ger_socdist_cpt_mean_diff, by='kreis') %>%
left_join(df_ger_socdist_cpt_var_diff, by='kreis') %>%
left_join(select(df_ger_onset_prev, kreis, onset_prev), by='kreis') %>%
left_join(select(df_ger_slope_prev, kreis, slope_prev), by='kreis')
# standardize mean/var differences
df_ger_cpt_socdist <- df_ger_cpt_socdist %>%
mutate(mean_diff_socdist = scale(mean_diff_socdist),
var_diff_socdist = scale(var_diff_socdist))
# handle censored data
df_ger_cpt_socdist <- df_ger_cpt_socdist %>%
mutate(cpt_day_socdist = ifelse(is.na(cpt_day_socdist),
as.numeric(diff(range(df_ger_socdist$date))),
cpt_day_socdist)) %>%
mutate(event = ifelse(cpt_day_socdist >= 60, 0, 1))
df_ger_cpt_socdist$cpt_day_socdist %>% hist()

df_ger_cpt_socdist$mean_diff_socdist %>% hist()

df_ger_cpt_socdist$var_diff_socdist %>% hist()

for(i in head(df_ger_socdist_cpt_results, 5)){
plot(i)
}




NA

Predicting change points with time-to-event regression
# predict hazard from personality
cox_cpt_socdist <- coxph(Surv(cpt_day_socdist, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_ger_cpt_socdist)
cox_cpt_socdist %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n, data = df_ger_cpt_socdist)
n= 400, number of events= 400
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.13238 1.14154 0.05555 2.383 0.0172 *
pers_c -0.04634 0.95472 0.05364 -0.864 0.3876
pers_e -0.08928 0.91459 0.05443 -1.640 0.1009
pers_a -0.05236 0.94898 0.04881 -1.073 0.2833
pers_n -0.08884 0.91499 0.05376 -1.653 0.0984 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.1415 0.876 1.0238 1.273
pers_c 0.9547 1.047 0.8594 1.061
pers_e 0.9146 1.093 0.8221 1.018
pers_a 0.9490 1.054 0.8624 1.044
pers_n 0.9150 1.093 0.8235 1.017
Concordance= 0.595 (se = 0.024 )
Likelihood ratio test= 10.81 on 5 df, p=0.06
Wald test = 10.76 on 5 df, p=0.06
Score (logrank) test = 10.71 on 5 df, p=0.06
# predict hazard from personality with controls
cox_cpt_socdist_ctrl <- coxph(Surv(cpt_day_socdist, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_cpt_socdist,)
cox_cpt_socdist_ctrl %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n + women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age + popdens,
data = df_ger_cpt_socdist)
n= 392, number of events= 392
(8 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.01147 1.01154 0.06432 0.178 0.85846
pers_c -0.04040 0.96041 0.06122 -0.660 0.50935
pers_e -0.16691 0.84628 0.06130 -2.723 0.00647 **
pers_a -0.02162 0.97861 0.05627 -0.384 0.70084
pers_n -0.05115 0.95013 0.06007 -0.852 0.39442
women 0.10582 1.11163 0.07640 1.385 0.16603
academics -0.01816 0.98201 0.10793 -0.168 0.86640
afd -0.03782 0.96288 0.08037 -0.471 0.63792
hospital_beds -0.19535 0.82255 0.06587 -2.965 0.00302 **
tourism_beds 0.08410 1.08774 0.05639 1.492 0.13583
gdp 0.14418 1.15509 0.12603 1.144 0.25262
manufact -0.14887 0.86168 0.09244 -1.610 0.10731
airport -0.09012 0.91382 0.06467 -1.394 0.16343
age -0.14891 0.86165 0.09656 -1.542 0.12304
popdens 0.17167 1.18729 0.08440 2.034 0.04196 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.0115 0.9886 0.8917 1.1474
pers_c 0.9604 1.0412 0.8518 1.0829
pers_e 0.8463 1.1816 0.7505 0.9543
pers_a 0.9786 1.0219 0.8764 1.0927
pers_n 0.9501 1.0525 0.8446 1.0688
women 1.1116 0.8996 0.9570 1.2912
academics 0.9820 1.0183 0.7948 1.2134
afd 0.9629 1.0385 0.8225 1.1272
hospital_beds 0.8226 1.2157 0.7229 0.9359
tourism_beds 1.0877 0.9193 0.9739 1.2148
gdp 1.1551 0.8657 0.9023 1.4787
manufact 0.8617 1.1605 0.7189 1.0328
airport 0.9138 1.0943 0.8050 1.0373
age 0.8616 1.1606 0.7131 1.0412
popdens 1.1873 0.8423 1.0063 1.4009
Concordance= 0.697 (se = 0.022 )
Likelihood ratio test= 62.2 on 15 df, p=1e-07
Wald test = 66.91 on 15 df, p=2e-08
Score (logrank) test = 68.7 on 15 df, p=8e-09
# predict hazard from personality with controls
cox_cpt_socdist_ctrl2 <- coxph(Surv(cpt_day_socdist, event) ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens + onset_prev + slope_prev,
data = df_ger_cpt_socdist)
cox_cpt_socdist_ctrl2 %>% summary()
Call:
coxph(formula = Surv(cpt_day_socdist, event) ~ pers_o + pers_c +
pers_e + pers_a + pers_n + women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age + popdens +
onset_prev + slope_prev, data = df_ger_cpt_socdist)
n= 392, number of events= 392
(8 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
pers_o 0.009217 1.009259 0.064871 0.142 0.88702
pers_c -0.041231 0.959607 0.061433 -0.671 0.50212
pers_e -0.172081 0.841911 0.062033 -2.774 0.00554 **
pers_a -0.014363 0.985739 0.057276 -0.251 0.80199
pers_n -0.037497 0.963197 0.061112 -0.614 0.53949
women 0.121990 1.129743 0.077951 1.565 0.11759
academics -0.041589 0.959264 0.109525 -0.380 0.70416
afd -0.021631 0.978601 0.081267 -0.266 0.79010
hospital_beds -0.189670 0.827232 0.066132 -2.868 0.00413 **
tourism_beds 0.082883 1.086415 0.056235 1.474 0.14052
gdp 0.175596 1.191956 0.127605 1.376 0.16879
manufact -0.170477 0.843263 0.093993 -1.814 0.06972 .
airport -0.077842 0.925111 0.065550 -1.188 0.23502
age -0.149096 0.861486 0.098774 -1.509 0.13118
popdens 0.157912 1.171064 0.084451 1.870 0.06150 .
onset_prev -0.006740 0.993283 0.005197 -1.297 0.19468
slope_prev -0.026992 0.973369 0.064662 -0.417 0.67637
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
pers_o 1.0093 0.9908 0.8888 1.1461
pers_c 0.9596 1.0421 0.8507 1.0824
pers_e 0.8419 1.1878 0.7455 0.9508
pers_a 0.9857 1.0145 0.8811 1.1028
pers_n 0.9632 1.0382 0.8545 1.0858
women 1.1297 0.8852 0.9697 1.3162
academics 0.9593 1.0425 0.7739 1.1890
afd 0.9786 1.0219 0.8345 1.1476
hospital_beds 0.8272 1.2089 0.7267 0.9417
tourism_beds 1.0864 0.9205 0.9730 1.2130
gdp 1.1920 0.8390 0.9282 1.5307
manufact 0.8433 1.1859 0.7014 1.0138
airport 0.9251 1.0810 0.8136 1.0519
age 0.8615 1.1608 0.7099 1.0455
popdens 1.1711 0.8539 0.9924 1.3819
onset_prev 0.9933 1.0068 0.9832 1.0035
slope_prev 0.9734 1.0274 0.8575 1.1049
Concordance= 0.7 (se = 0.022 )
Likelihood ratio test= 65.24 on 17 df, p=1e-07
Wald test = 69.97 on 17 df, p=2e-08
Score (logrank) test = 71.77 on 17 df, p=1e-08
Linear models predicting mean differences
lm_meandiff_socdist <- lm(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n,
data = df_ger_cpt_socdist)
lm_meandiff_socdist %>% summary()
Call:
lm(formula = mean_diff_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n, data = df_ger_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-2.6704 -0.6077 -0.0112 0.5246 3.9074
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.881e-16 4.603e-02 0.000 1.0000
pers_o 3.573e-01 4.928e-02 7.251 2.21e-12 ***
pers_c -5.383e-02 5.227e-02 -1.030 0.3037
pers_e 5.436e-02 5.253e-02 1.035 0.3014
pers_a -1.307e-02 5.224e-02 -0.250 0.8026
pers_n -1.268e-01 5.423e-02 -2.338 0.0199 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9206 on 394 degrees of freedom
Multiple R-squared: 0.1631, Adjusted R-squared: 0.1525
F-statistic: 15.36 on 5 and 394 DF, p-value: 8.43e-14
lm_meandiff_socdist %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.07589246 0.07589246
pers_o 0.27605447 0.43854018
pers_c -0.14000439 0.03234850
pers_e -0.03224219 0.14095947
pers_a -0.09919902 0.07306852
pers_n -0.21617769 -0.03736628
lm_meandiff_socdist_ctrl <- lm(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens,
data = df_ger_cpt_socdist,)
lm_meandiff_socdist_ctrl %>% summary()
Call:
lm(formula = mean_diff_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n + women + academics + afd + hospital_beds + tourism_beds +
gdp + manufact + airport + age + popdens, data = df_ger_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-2.82186 -0.48179 -0.06099 0.41463 2.54031
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.005219 0.036964 -0.141 0.887799
pers_o 0.106164 0.045638 2.326 0.020538 *
pers_c 0.048613 0.044214 1.099 0.272256
pers_e -0.037110 0.043289 -0.857 0.391844
pers_a -0.004674 0.044377 -0.105 0.916179
pers_n 0.014518 0.044880 0.323 0.746510
women 0.124196 0.054147 2.294 0.022360 *
academics 0.081958 0.072270 1.134 0.257493
afd -0.146522 0.053891 -2.719 0.006854 **
hospital_beds -0.179132 0.049722 -3.603 0.000357 ***
tourism_beds 0.026876 0.041676 0.645 0.519393
gdp 0.322525 0.085179 3.786 0.000178 ***
manufact -0.044887 0.067655 -0.663 0.507442
airport -0.059031 0.046297 -1.275 0.203079
age -0.326924 0.068682 -4.760 2.77e-06 ***
popdens -0.016479 0.059794 -0.276 0.783013
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7311 on 376 degrees of freedom
(8 observations deleted due to missingness)
Multiple R-squared: 0.4871, Adjusted R-squared: 0.4666
F-statistic: 23.81 on 15 and 376 DF, p-value: < 2.2e-16
lm_meandiff_socdist_ctrl %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.06616945 0.05573185
pers_o 0.03091038 0.18141662
pers_c -0.02429231 0.12151912
pers_e -0.10848908 0.03426939
pers_a -0.07784795 0.06850047
pers_n -0.05948507 0.08852057
women 0.03491147 0.21347956
academics -0.03720884 0.20112538
afd -0.23538302 -0.05766115
hospital_beds -0.26111965 -0.09714365
tourism_beds -0.04184386 0.09559660
gdp 0.18207149 0.46297894
manufact -0.15644458 0.06667126
airport -0.13537018 0.01730886
age -0.44017449 -0.21367285
popdens -0.11507384 0.08211648
lm_meandiff_socdist_ctrl2 <- lm(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens + onset_prev + slope_prev,
data = df_ger_cpt_socdist)
lm_meandiff_socdist_ctrl2 %>% summary()
Call:
lm(formula = mean_diff_socdist ~ pers_o + pers_c + pers_e + pers_a +
pers_n + women + academics + afd + hospital_beds + tourism_beds +
gdp + manufact + airport + age + popdens + onset_prev + slope_prev,
data = df_ger_cpt_socdist)
Residuals:
Min 1Q Median 3Q Max
-2.7924 -0.4819 -0.0360 0.4080 2.6169
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.264112 0.201982 1.308 0.191811
pers_o 0.108998 0.044269 2.462 0.014259 *
pers_c 0.051255 0.042940 1.194 0.233373
pers_e -0.056367 0.042250 -1.334 0.182976
pers_a -0.020230 0.043157 -0.469 0.639515
pers_n 0.009412 0.043847 0.215 0.830160
women 0.098099 0.052769 1.859 0.063809 .
academics 0.084990 0.070407 1.207 0.228148
afd -0.135862 0.052495 -2.588 0.010026 *
hospital_beds -0.190103 0.048586 -3.913 0.000108 ***
tourism_beds 0.036003 0.040470 0.890 0.374241
gdp 0.334790 0.082937 4.037 6.58e-05 ***
manufact -0.086818 0.066355 -1.308 0.191540
airport -0.087019 0.045262 -1.923 0.055297 .
age -0.278359 0.067715 -4.111 4.85e-05 ***
popdens 0.009521 0.058234 0.163 0.870216
onset_prev -0.004807 0.003549 -1.354 0.176427
slope_prev 0.210948 0.042472 4.967 1.04e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7091 on 374 degrees of freedom
(8 observations deleted due to missingness)
Multiple R-squared: 0.5201, Adjusted R-squared: 0.4983
F-statistic: 23.84 on 17 and 374 DF, p-value: < 2.2e-16
lm_meandiff_socdist_ctrl2 %>% confint(level=0.9)
5 % 95 %
(Intercept) -0.06894364 0.597167453
pers_o 0.03600153 0.181994243
pers_c -0.01955027 0.122060396
pers_e -0.12603448 0.013301070
pers_a -0.09139314 0.050932815
pers_n -0.06288910 0.081712228
women 0.01108590 0.185112674
academics -0.03110714 0.201088040
afd -0.22242240 -0.049301807
hospital_beds -0.27021921 -0.109987126
tourism_beds -0.03072965 0.102735952
gdp 0.19803273 0.471548214
manufact -0.19623295 0.022596103
airport -0.16165358 -0.012383791
age -0.39001650 -0.166702289
popdens -0.08650307 0.105545112
onset_prev -0.01065842 0.001045275
slope_prev 0.14091460 0.280982005
CRF predicting mean difference
ctrls <- cforest_unbiased(ntree=500, mtry=5)
crf_meandiff_socdist <- cforest(mean_diff_socdist ~
pers_o + pers_c + pers_e + pers_a + pers_n +
women + academics + afd + hospital_beds +
tourism_beds + gdp + manufact + airport + age +
popdens + onset_prev + slope_prev,
data = df_ger_cpt_socdist %>% drop_na(),
controls = ctrls)
crf_meandiff_socdist_varimp <- varimp(crf_meandiff_socdist, nperm = 1)
crf_meandiff_socdist_varimp_cond <- varimp(crf_meandiff_socdist, conditional = T, nperm = 1)
crf_meandiff_socdist_varimp
pers_o pers_c pers_e pers_a pers_n women academics
0.0180211122 0.0015870098 0.0027043254 0.0005814055 0.0026234653 0.0099984261 0.1482181959
afd hospital_beds tourism_beds gdp manufact airport age
0.1397866414 0.0112907121 0.0004702307 0.1025281549 0.0089789021 0.0165751547 0.2422748719
popdens onset_prev slope_prev
0.0433761825 0.0016622854 0.0148192230
crf_meandiff_socdist_varimp %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

crf_meandiff_socdist_varimp_cond
pers_o pers_c pers_e pers_a pers_n women academics
1.656467e-02 2.611214e-03 2.404216e-03 -6.376514e-05 2.583184e-03 8.034409e-03 1.517586e-01
afd hospital_beds tourism_beds gdp manufact airport age
1.486145e-01 1.175789e-02 2.029922e-03 9.026232e-02 1.075169e-02 1.350605e-02 2.393327e-01
popdens onset_prev slope_prev
4.531639e-02 3.076339e-03 1.778331e-02
crf_meandiff_socdist_varimp_cond %>% as.data.frame() %>%
rownames_to_column('variable') %>%
ggplot(aes(x=variable, y=.)) +
geom_bar(stat = 'identity') +
theme(axis.text.x = element_text(angle = 90))

Export data
ger_list_results <- list(cox_onset_prev, cox_onset_prev_ctrl,
lm_slope_prev, lm_slope_prev_ctrl,
cox_cpt_socdist, cox_cpt_socdist_ctrl, cox_cpt_socdist_ctrl2,
lm_meandiff_socdist, lm_meandiff_socdist_ctrl, lm_meandiff_socdist_ctrl2)
results_names <- list('cox_onset_prev', 'cox_onset_prev_ctrl',
'lm_slope_prev', 'lm_slope_prev_coef',
'cox_cpt_socdist', 'cox_cpt_socdist_ctrl', 'cox_cpt_socdist_ctrl2',
'lm_meandiff_socdist', 'lm_meandiff_socdist_ctrl', 'lm_meandiff_socdist_ctrl2')
names(ger_list_results) <- results_names
save(ger_list_results, file="ger_list_results.RData")
write_csv(df_ger_slope_prev, 'df_ger_slope_prev.csv')
write_csv(df_ger_cpt_socdist, 'df_ger_cpt_socdist.csv')
---
title: "COVID19 GER"
author: "Heinrich Peters"
date: "4/23/2020"
output: html_notebook
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

# MAC
 knitr::opts_knit$set(root.dir = '/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/GER')
 
library(lmerTest)
library(nlme)
library(psych)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(party)

```

# Prepare data

### Prevalence
```{r}

df_ger_prev <- read_csv('Germany_timeseries_prep.csv')

df_ger_prev <- df_ger_prev %>% mutate(date = as.Date(date, "%d%b%Y"),
                                  kreis = as.character(kreis)) %>% 
  dplyr::select(kreis, date, rate_day)

df_ger_prev
```

### Scoial distancing
```{r}

df_ger_socdist <- read_csv('Germany_socdist_fb_kreis.csv')

df_ger_socdist <- df_ger_socdist %>% 
  rename(socdist_single_tile = all_day_ratio_single_tile_users) %>%
  select(kreis, date, socdist_single_tile) %>% 
  mutate(kreis = as.character(kreis))

df_ger_socdist$date %>% summary()

```

### Personality 
```{r}

df_ger_pers <- read_csv('Germany_timeseries_prep.csv')

df_ger_pers <- df_ger_pers %>% 
  select(kreis, open, sci, extra, agree, neuro) %>%
  dplyr::rename(pers_o = open, 
         pers_c = sci,
         pers_e = extra,
         pers_a = agree,
         pers_n = neuro) %>%
  distinct() %>%
  mutate(kreis = as.character(kreis))

df_ger_pers

```


### Controls 
```{r}

df_ger_ctrl <- read.csv2('Germany_controls.csv', sep = ';', dec=',')

df_ger_ctrl <- df_ger_ctrl %>% select(-kreis_nme) %>%
    mutate(kreis = as.character(kreis),
           popdens = popdens %>% 
             as.character() %>%
             str_replace('\\.', '')%>%
             as.numeric())

df_ger_ctrl

```

### Merge prevalence data 
```{r}
# create sequence of dates
date_sequence <- seq.Date(min(df_ger_prev$date),
                          max(df_ger_prev$date), 1)
                     
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence)) 
names(df_dates) <- c('date', 'time')


# merge prevalence data
df_ger_prev <- df_ger_prev %>% 
  inner_join(df_ger_pers, by = 'kreis') %>%
  inner_join(df_ger_ctrl, by = 'kreis') %>%
  merge(df_dates, by='date') %>% 
  arrange(kreis)

df_ger_prev
```

### Merge socdist data
```{r}

# create sequence of dates
date_sequence <- seq.Date(min(df_ger_socdist$date),
                          max(df_ger_socdist$date), 1)
                     
# create data frame with time sequence
df_dates = tibble(date_sequence, 1:length(date_sequence)) 
names(df_dates) <- c('date', 'time')

# merge socdist data
df_ger_socdist <- df_ger_socdist %>% 
  inner_join(df_ger_pers, by = 'kreis') %>%
  inner_join(df_ger_ctrl, by = 'kreis') %>% 
  merge(df_dates, by='date') %>% 
  arrange(kreis)

df_ger_socdist

```

### Control for weekend effect 
```{r}

easter <- seq.Date(as.Date('2020-04-10'), as.Date('2020-04-13'), 1)

df_ger_loess <- df_ger_socdist %>% 
  mutate(weekday = format(date, '%u')) %>% 
  filter(!(weekday %in% c('6','7') | date %in% easter)) %>% 
  split(.$kreis) %>%
  map(~ loess(socdist_single_tile ~ time, data = .)) %>%
  map(predict, 1:max(df_ger_socdist$time)) %>% 
  bind_rows() %>% 
  gather(key = 'kreis', value = 'loess') %>% 
  group_by(kreis) %>% 
  mutate(time = row_number())

df_ger_socdist <- df_ger_socdist %>% merge(df_ger_loess, by=c('kreis', 'time')) %>% 
  mutate(weekday = format(date, '%u')) %>% 
  mutate(socdist_single_tile_clean = ifelse(weekday %in% c('6','7') | date %in% easter, 
                                            loess, socdist_single_tile)) %>%
  arrange(kreis, time) %>% 
  select(-weekday)

df_ger_socdist %>% drop_na()

```


# Explore data

### Plot prevalence over time
```{r}

df_ger_prev %>% ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=kreis, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall prevalence over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_ger_prev %>% mutate(prev_tail = cut(.[[i]], 
      breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
      labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(prev_tail != 'center') %>%
  ggplot(aes(x=time, y=rate_day)) + 
  geom_point(aes(col=kreis, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~prev_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```


### Plot social distancing (single tile) over time

```{r}

df_ger_socdist %>% ggplot(aes(x=time, y=socdist_single_tile_clean)) + 
  geom_point(aes(col=kreis, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  theme(legend.position="none") +
  ggtitle("Overall social distancing (single tile) over time")

pers <- c('pers_o', 'pers_c', 'pers_e', 'pers_a', 'pers_n')

for (i in pers){

gg <- df_ger_socdist %>% mutate(prev_tail = cut(.[[i]], 
      breaks = c(-Inf, quantile(.[[i]], 0.2), quantile(.[[i]], 0.8), Inf),
      labels = c('lower tail', 'center', 'upper tail'))) %>% 
  filter(prev_tail != 'center') %>%
  ggplot(aes(x=time, y=socdist_single_tile_clean)) + 
  geom_point(aes(col=kreis, size=popdens)) + 
  geom_smooth(method="loess", se=T) + 
  facet_wrap(~prev_tail) + 
  theme(legend.position="none") +
  ggtitle(i)

print(gg)
}

```


```{r}

df_ger_socdist <- df_ger_socdist %>% mutate(socdist_single_tile = socdist_single_tile_clean) %>% 
  select(-loess, -socdist_single_tile_clean)

```


### Correlations
```{r}

df_ger_prev %>% group_by(kreis) %>% 
  summarize_if(is.numeric, mean) %>% 
  select(-kreis, -time) %>% 
  cor(use='pairwise.complete') %>% 
  round(3) %>% as.data.frame()

df_ger_socdist %>% group_by(kreis) %>% 
  summarize_if(is.numeric, mean) %>% 
  select(-kreis, -time) %>% 
  cor(use='pairwise.complete') %>% 
  round(3) %>% as.data.frame()
 
 
```

## Rescale data
```{r}

lvl2_scaled <- df_ger_prev %>% 
  dplyr::select(-time, -rate_day, -date) %>% 
  distinct() %>% 
  mutate_at(vars(-kreis), scale)
  
lvl1_scaled <- df_ger_prev %>% select(kreis, time, rate_day)

df_ger_prev_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'kreis')

df_ger_prev_scaled

```

```{r}

lvl2_scaled <- df_ger_socdist %>% 
  dplyr::select(-time, -socdist_single_tile, -date) %>% 
  distinct() %>% 
  mutate_at(vars(-kreis), scale)
  
lvl1_scaled <- df_ger_socdist %>% select(kreis, time, socdist_single_tile)

df_ger_socdist_scaled <- plyr::join(lvl1_scaled, lvl2_scaled, by = 'kreis')

df_ger_socdist_scaled

```

# Predict Prevalence
### Extract first day of covid outbreak
```{r}

# get onset day
df_ger_onset_prev <- df_ger_prev_scaled %>% 
  group_by(kreis) %>% 
  mutate(rate_cs = cumsum(rate_day)) %>% 
  filter(rate_cs > 0) %>%
  summarize(onset_prev = min(time))
  
# merge with county data
df_ger_onset_prev <- df_ger_prev_scaled %>% 
  select(-time, -rate_day) %>%
  distinct() %>% 
  left_join(df_ger_onset_prev, by = 'kreis')

# handle censored data
df_ger_onset_prev <- df_ger_onset_prev %>% 
  mutate(event = ifelse(is.na(onset_prev), 0, 1)) %>% 
  mutate(onset_prev = replace_na(onset_prev, as.numeric(diff(range(df_ger_prev$date)))+1))

df_ger_onset_prev

```

### Extract slopes
```{r}

# cut time series before onset
df_ger_prev_scaled <- df_ger_prev_scaled %>% 
  group_by(kreis) %>% 
  mutate(rate_cs = cumsum(rate_day)) %>% 
  filter(rate_cs > 0) %>%
  mutate(time = time-min(time)+1) %>%
  ungroup() %>%
  filter(time <= 30) %>%
  select(-rate_cs)

# drop counties with little data
df_ger_prev_scaled <- df_ger_prev_scaled %>%
  group_by(kreis) %>%
  filter(n() == 30) %>%
  ungroup()

# extract slope prevalence
df_ger_slope_prev <- df_ger_prev_scaled %>% 
  split(.$kreis) %>% 
  map(~ lm(rate_day ~ time, data = .)) %>%
  map(coef) %>% 
  map_dbl('time') %>% 
  as.data.frame() %>% 
  rownames_to_column('kreis') %>% 
  rename(slope_prev = '.')

# merge with county data
df_ger_slope_prev <- df_ger_prev_scaled %>% 
  select(-time, -rate_day) %>%
  distinct() %>% 
  inner_join(df_ger_slope_prev, by = 'kreis') %>%
  drop_na()

# standardize slopes
df_ger_slope_prev <- df_ger_slope_prev %>% 
  mutate(slope_prev = scale(slope_prev))

```


### Explore distributions
```{r}

df_ger_onset_prev %>% ggplot(aes(onset_prev)) + geom_histogram()
df_ger_slope_prev %>% ggplot(aes(slope_prev)) + geom_histogram()

```


## Predict COVID onset with time-to-event regression 
```{r}

# predict onset from personality
cox_onset_prev <- coxph(Surv(onset_prev, event) ~ 
                          pers_o + pers_c + pers_e + pers_a + pers_n, 
                        data = df_ger_onset_prev)
cox_onset_prev %>% summary()

# predict onset from personality with controls
cox_onset_prev_ctrl <- coxph(Surv(onset_prev, event) ~ 
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                             data = df_ger_onset_prev)
cox_onset_prev_ctrl %>% summary()

```

## Predict prevalence slopes with linear models
```{r}

# predict slopes from personality
lm_slope_prev <- lm(slope_prev ~ pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_ger_slope_prev)
lm_slope_prev %>% summary()
lm_slope_prev %>% confint(level=0.9)

# predict slopes from personality with controls
lm_slope_prev_ctrl <- lm(slope_prev ~  
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                         data = df_ger_slope_prev)
lm_slope_prev_ctrl %>% summary()
lm_slope_prev_ctrl %>% confint(level=0.9)

```

### CRF predicting slopes
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_slope_prev <- cforest(slope_prev ~  
                               pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                           data = df_ger_slope_prev, 
                         controls = ctrls)

crf_slope_prev_varimp <- varimp(crf_slope_prev, nperm = 1)
crf_slope_prev_varimp_cond <- varimp(crf_slope_prev, conditional = T, nperm = 1)

crf_slope_prev_varimp
crf_slope_prev_varimp %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') + 
  theme(axis.text.x = element_text(angle = 90))

crf_slope_prev_varimp_cond
crf_slope_prev_varimp_cond %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```

## Predict Social Distancing
### Change point analysis
```{r}

# keep only counties with full data
kreis_complete <- df_ger_socdist_scaled %>% 
  group_by(kreis) %>% 
  summarize(n = n()) %>% 
  filter(n==max(.$n)) %>% 
  .$kreis

# run changepoint analysis
df_ger_socdist_cpt_results <- df_ger_socdist_scaled %>% 
  select(kreis, socdist_single_tile) %>%
  filter(kreis %in% kreis_complete) %>% 
  split(.$kreis) %>%
  map(~ cpt.meanvar(as.vector(.$socdist_single_tile),
                    #penalty = 'Asymptotic',
                    class=TRUE,
                    param.estimates=TRUE,
                    Q=1,
                    test.stat = 'Normal'))

# calculate change point
df_ger_socdist_cpt_day <- df_ger_socdist_cpt_results %>% 
  map(cpts) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(cpt_day_socdist = '.') %>%
  rownames_to_column('kreis')

# calculate mean differences
df_ger_socdist_cpt_mean_diff <- df_ger_socdist_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$mean) %>% 
  map(~ .[2]-.[1]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(mean_diff_socdist = '.') %>%
  rownames_to_column('kreis')

# calculate varaince differences
df_ger_socdist_cpt_var_diff <- df_ger_socdist_cpt_results %>% 
  map(param.est) %>% 
  map(~ .$variance) %>% 
  map(~ .[2]-.[1]) %>% 
  unlist() %>% 
  as.data.frame() %>% 
  rename(var_diff_socdist = '.') %>%
  rownames_to_column('kreis')

# merge with county data
df_ger_cpt_socdist <- df_ger_socdist_scaled %>% 
  select(-time, -socdist_single_tile) %>%
  distinct() %>% 
  left_join(df_ger_socdist_cpt_day, by='kreis') %>%
  left_join(df_ger_socdist_cpt_mean_diff, by='kreis') %>%
  left_join(df_ger_socdist_cpt_var_diff, by='kreis') %>%
  left_join(select(df_ger_onset_prev, kreis, onset_prev), by='kreis') %>%
  left_join(select(df_ger_slope_prev, kreis, slope_prev), by='kreis') 

# standardize mean/var differences
df_ger_cpt_socdist <- df_ger_cpt_socdist %>% 
  mutate(mean_diff_socdist = scale(mean_diff_socdist),
         var_diff_socdist = scale(var_diff_socdist))

# handle censored data
df_ger_cpt_socdist <- df_ger_cpt_socdist %>% 
  mutate(cpt_day_socdist = ifelse(is.na(cpt_day_socdist), 
                                  as.numeric(diff(range(df_ger_socdist$date))), 
                                  cpt_day_socdist)) %>% 
  mutate(event = ifelse(cpt_day_socdist >= 60, 0, 1))

```

```{r}
df_ger_cpt_socdist$cpt_day_socdist %>% hist()
df_ger_cpt_socdist$mean_diff_socdist %>% hist()
df_ger_cpt_socdist$var_diff_socdist %>% hist()

```

```{r}

for(i in head(df_ger_socdist_cpt_results, 5)){
  plot(i)
}

```


# Predicting change points with time-to-event regression 
```{r}

# predict hazard from personality
cox_cpt_socdist <- coxph(Surv(cpt_day_socdist, event) ~ 
                           pers_o + pers_c + pers_e + pers_a + pers_n, 
                  data = df_ger_cpt_socdist)
cox_cpt_socdist %>% summary()

# predict hazard from personality with controls
cox_cpt_socdist_ctrl <- coxph(Surv(cpt_day_socdist, event) ~ 
                                pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                           data = df_ger_cpt_socdist,)
cox_cpt_socdist_ctrl %>% summary()

# predict hazard from personality with controls
cox_cpt_socdist_ctrl2 <- coxph(Surv(cpt_day_socdist, event) ~ 
                                 pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens + onset_prev + slope_prev,
                  data = df_ger_cpt_socdist)
cox_cpt_socdist_ctrl2 %>% summary()

```

### Linear models predicting mean differences
```{r}

lm_meandiff_socdist <- lm(mean_diff_socdist ~ 
                            pers_o + pers_c + pers_e + pers_a + pers_n, 
                         data = df_ger_cpt_socdist)
lm_meandiff_socdist %>% summary()
lm_meandiff_socdist %>% confint(level=0.9)

lm_meandiff_socdist_ctrl <- lm(mean_diff_socdist ~ 
                                 pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens,
                           data = df_ger_cpt_socdist,)
lm_meandiff_socdist_ctrl %>% summary()
lm_meandiff_socdist_ctrl %>% confint(level=0.9)

lm_meandiff_socdist_ctrl2 <- lm(mean_diff_socdist ~ 
                                  pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens + onset_prev + slope_prev,
                  data = df_ger_cpt_socdist)
lm_meandiff_socdist_ctrl2 %>% summary()
lm_meandiff_socdist_ctrl2 %>% confint(level=0.9)

```

### CRF predicting mean difference
```{r}

ctrls <- cforest_unbiased(ntree=500, mtry=5)

crf_meandiff_socdist <- cforest(mean_diff_socdist ~ 
                                  pers_o + pers_c + pers_e + pers_a + pers_n +
                               women + academics + afd + hospital_beds +
                               tourism_beds + gdp + manufact + airport + age +
                               popdens + onset_prev + slope_prev,
                              data = df_ger_cpt_socdist %>% drop_na(),
                         controls = ctrls)

crf_meandiff_socdist_varimp <- varimp(crf_meandiff_socdist, nperm = 1)
crf_meandiff_socdist_varimp_cond <- varimp(crf_meandiff_socdist, conditional = T, nperm = 1)

crf_meandiff_socdist_varimp
crf_meandiff_socdist_varimp %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

crf_meandiff_socdist_varimp_cond
crf_meandiff_socdist_varimp_cond %>% as.data.frame() %>% 
  rownames_to_column('variable') %>%
  ggplot(aes(x=variable, y=.)) +
  geom_bar(stat = 'identity') +
  theme(axis.text.x = element_text(angle = 90))

```

### Export data 
```{r}

ger_list_results <- list(cox_onset_prev, cox_onset_prev_ctrl, 
     lm_slope_prev, lm_slope_prev_ctrl, 
     cox_cpt_socdist, cox_cpt_socdist_ctrl, cox_cpt_socdist_ctrl2,
     lm_meandiff_socdist, lm_meandiff_socdist_ctrl, lm_meandiff_socdist_ctrl2)

results_names <- list('cox_onset_prev', 'cox_onset_prev_ctrl', 
     'lm_slope_prev', 'lm_slope_prev_ctrl', 
     'cox_cpt_socdist', 'cox_cpt_socdist_ctrl', 'cox_cpt_socdist_ctrl2',
     'lm_meandiff_socdist', 'lm_meandiff_socdist_ctrl', 'lm_meandiff_socdist_ctrl2')

names(ger_list_results) <- results_names

save(ger_list_results, file="ger_list_results.RData")

```

```{r}

write_csv(df_ger_slope_prev, 'df_ger_slope_prev.csv')
write_csv(df_ger_cpt_socdist, 'df_ger_cpt_socdist.csv')

```